Lab 4

#Load Libraries

library (tidyverse)

## ── Attaching packages ─────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.2.1     ✓ purrr   0.3.3
## ✓ tibble  2.1.3     ✓ dplyr   0.8.4
## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.4.0

## ── Conflicts ────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

#Load Files

SNPs<- read.table("23andMe_complete.txt", header = TRUE, sep = "\t")

#to adjust figure size {r, fig.width = 6, fig.height = 6}

SNPs$chromosome = ordered(SNPs$chromosome, levels=c(seq(1, 22), "X", "Y", "MT"))
ggplot(data = SNPs) + 
  geom_bar(mapping = aes(x = genotype, fill = chromosome)) + 
  coord_polar() +
  ggtitle("Total SNPs for each genotype") +
  ylab("Total number of SNPs") +
  xlab("Genotype")

#Graphic Output #Plot graph to a pdf outputfile

pdf("SNP_example_plot.pdf", width=6, height=3)
ggplot(data = SNPs) + 
  geom_bar(mapping = aes(x = chromosome, fill = genotype))
dev.off()

## quartz_off_screen 
##                 2

Plot graph to a png outputfile

# Plot graph to a png outputfile
ppi <- 300
png("SNP_example_plot.png", width=6*ppi, height=6*ppi, res=ppi)
ggplot(data = SNPs) + 
  geom_bar(mapping = aes(x = chromosome, fill = genotype))
dev.off()

## quartz_off_screen 
##                 2

RMarkdown loading images

Genotype counts per chromosome

# Interactive graphs and tables in RMarkdown reports #Version 1

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

p <- ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) + 
  geom_point()
ggplotly(p)

#Version 2

library(plotly)
ggplotly(
  ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) + 
    geom_point()
 )

#DT

library(DT)
datatable(iris)

Exercise

Exercise 1

SNPs<- read.table("23andMe_complete.txt", header = TRUE, sep = "\t")
p<- ggplot(SNPs,aes(chromosome)) +
  geom_bar(fill = "blue") +
  ggtitle("Total SNPs for each chromosome") +
  ylab("Total number of SNPs") +
  xlab("chromosome")
p

Exercise 2

mycolor<-c("AA"="blue", "AC"="blue", "AG"="blue", "AT"="blue", "CC"="blue", "CG"="blue", "CT"="blue", "GG"="blue", "GT"="blue", "TT"="blue","A"="pink", "C"="pink", "G"="pink", "T"="pink", "D"="orange", "DD"="orange", "DI"="orange","I"="orange","II"="orange","--"="green")
ggplot(SNPs, aes(chromosome, fill = genotype))+
  geom_bar(color = "black")+
  ggtitle("Total SNPs count for each chromosome")+
  ylab("SNP count")+
  xlab("Chromosome")+
  scale_fill_manual(values=c(mycolor)) 

Exercise 3

ppi <- 300
png("ex3_plot", width=6*ppi, height=6*ppi, res=ppi)
ggplot(data = SNPs, aes(chromosome, fill = genotype)) + 
  geom_bar(position = "dodge")
dev.off()

## quartz_off_screen 
##                 2

Exercise 4

SNPs$chromosome=ordered(SNPs$chromosome, levels=c(seq(1,22),"X","Y","MT"))
ggplot(SNPs,aes(chromosome,fill=genotype))+
  geom_bar(position="dodge")+
  facet_wrap(~chromosome, scales="free")+
  ggtitle("SNP Count for Each Type of Chromosome")+
  ylab("SNP Count(Thousands)")+
  xlab("Type of Chromosome")

# Exercise 5

library(plotly)
SNPs$chromosome=ordered(SNPs$chromosome, levels=c(seq(1,22),"X","Y","MT"))
ggplotly(
  ggplot(SNPs,aes(chromosome,fill=genotype))+
  geom_bar(position="dodge")+
  facet_wrap(~chromosome, scales="free")
)

# Exercise 6

chromosome_subset <- subset(SNPs, chromosome == "Y")
datatable(chromosome_subset)

## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html